Electric System Impacts of Utility Large-Scale Investment in Building Energy Efficiency

Transcription

1 Electric System Impacts of Utility Large-Scale Investment in Building Energy Efficiency Dave Anderson PNNL ACEEE National Conference on Energy Efficiency as a Resource 9//

2 Electric System Impacts of Utility Large-Scale Investment in Building Energy Efficiency Forthcoming PNNL report: Authors Dave Anderson Brian Boyd Jim Cabe Benton Russell Laurel Schmidt Sponsored by DOE Building Technologies Program Not affiliated with nor addressing Smart Grid

3 The questions How much can adding large-scale energy efficiency resource deployment actually delay or avoid new bricks and mortar generation resources at the utility system level? What happens to dispatch in four geographically representative utility service areas, accounting for transaction behavior? How does the emissions profile change in each of the four example utility service areas?

4 Issues for Energy Efficiency Resources Institutional barriers abound for large-scale deployment Compensation model for cost recovery Rate-payer financing of new resources RPS in many states, but no accompanying recovery system What to do in deregulated states? How to acquire LARGE amounts of EE resource? program design Perceptions that favor central station for reliability and risk avoidance Market barriers also abound this is just RESIDENTIAL Impose major appliance decisions on consumers Consumer choice versus command and control Rate-payer financed? Mobility of the resource Reliability of the resource

5 The Model: PROMOD IV by Ventyx PROMOD IV is the industry-leading Fundamental Electric Market Simulation software, incorporating extensive details for understanding generating unit operating characteristics, transmission grid topology and constraints, and market system operations. from the Ventyx website. Allows zonal or nodal market (bus level) behavior to be simulated Comprehensive database of generators by utility service area Database is fully customizable if you have the data Visibility at all points of the analysis As much or as little summary detail as desired Utility system load forecasting and resource acquisition Simulates purchases and sales in the wholesale market

6 Criteria for Example Utilities Large enough to potentially consider new central station baseload Diverse fuel and technology mix of generation resources Geographically representative Regulated, no merchant generation Load forecasts are available

7 Example Utilities: Installed Capacity by Fuel Other Wind Solar Renew Oil Nuclear Hydro Gas Coal Southwest Southeast Midwest Northeast Source: Ventyx PowerBase

8 Implementation Assumptions Utility can finance (and recover) costs of acquisition and installation of new major appliances for upwards of, residences or more Appliance upgrades would meet current Energy Star labeling criteria Project budget would be set at $- billion (overnight) and require a -year build-out, similar to a large central station power plant Adequate supply of installers and inspectors could be trained and available to support the build out Volume purchase contracts with manufacturers would be implemented and annual unit installation goals would be met. Annual maintenance program to recommission

9 Residential Measures Applied Current Energy Star-compliant models installed across residential customer base CFL Lighting Refrigerator Stove, Range, Oven Water heater HVAC unit Television Allowance for unrealizable installations or previous adopters Allowance for measure degradation Stock accounting to only 9

10 The Model: Getting Residential Loads Right

11 The Model: Getting Residential Loads Right Load (MW) Heating Load Cooling Load Temperature- Independent Load Temperature ( C)

12 The Model: Getting Residential Loads Right

13 End Use Load Shapes: Some Examples RSFSCE: Residential Space Conditioning Heat Pump (South) NEMS Default RSFTVEW: Residential Television RSFWHO: Residential Water Heating Base/Conventional NEMS Default Percentage of Daily End Use Consumption 9 Source: AEO NEMS input file: alllsr.v..txt Percentage of Daily End Use Consumption Source: AEO NEMS input file: alllsr.v..txt Percentage of Daily End Use Consumption Source: AEO NEMS input file: alllsr.v..txt region Los Angeles bldtyp Large Office enduse Space Cooling vintage Existing bundle Standard HVAC month August daytype WEEK DAY res RSFSCE com COCCAL Cht_vctr LoadShapes!$F$:$AC$ LoadShapes!$F$:$AC$ LoadShapes!$F$:$AC$ Res Shape Com Shape region Los Angeles bldtyp Large Office enduse Space Cooling vintage Existing TV bundle & Standard Gadgets HVAC month March daytype WEEK DAY res RSFTVEW com COCCAL Cht_vctr LoadShapes!$F$9:$AC$9 LoadShapes!$F$9:$AC$9 LoadShapes!$F$9:$AC$9 Res Shape 9 Com Shape 9 region Los Angeles bldtyp Large Office enduse Space Cooling vintage Existing bundle Standard HVAC month March daytype WEEK DAY res RSFWHO com COCCAL Cht_vctr LoadShapes!$F$9:$AC$9 LoadShapes!$F$9:$AC$9 LoadShapes!$F$9:$AC$9 Res Shape 9 Com Shape 9 AC Water Heating Hr 9 9 August WEEK DAY RSFEHO: Residential Elec Resistance Heat Heat Pump (North) NEMS Default Percentage of Daily End Use Consumption Source: AEO NEMS input file: alllsr.v..txt Hr 9 9 March WEEK DAY RSFRFW: Residential Refrigeration Base/Conventional NEMS Default Percentage of Daily End Use Consumption Source: AEO NEMS input file: alllsr.v..txt Hr 9 9 March WEEK DAY RSFWSW: Residential Clothes Washer Base/Conventional Percentage of Daily End Use Consumption Source: AEO NEMS input file: alllsr.v..txt region Los Angeles bldtyp Large Office enduse Space Cooling vintage Existing bundle Standard HVAC month January daytype WEEK DAY res RSFEHO com COCCAL Cht_vctr LoadShapes!$F$:$AC$ LoadShapes!$F$:$AC$ LoadShapes!$F$9:$AC$9 Res Shape Com Shape 9 region Los Angeles bldtyp Large Office enduse Space Cooling vintage Existing bundle Standard HVAC month March daytype WEEK DAY res 9 RSFRFW com COCCAL Cht_vctr LoadShapes!$F$9:$AC$9 LoadShapes!$F$9:$AC$9 LoadShapes!$F$9:$AC$9 Res Shape 9 Com Shape 9 9 region Los Angeles bldtyp Large Office enduse Space Cooling vintage Existing bundle Standard HVAC month August daytype WEEK DAY res RSFWSW com COCCAL Cht_vctr LoadShapes!$F$9:$AC$9 LoadShapes!$F$9:$AC$9 LoadShapes!$F$:$AC$ Res Shape 9 Com Shape Heating Refrigeration Clothes Washer Hr 9 9 Hr 9 9 Hr 9 9 January WEEK DAY March WEEK DAY August WEEK DAY

14 Load Duration Curves for Example Utilities Proportion of Annual Peak MW NE SW MW SE Proportion of Annual Hours

15 Modeling Savings

16 , Proportion of Maximum Savings Savings Duration: Southwest Example MW Summer Winter Proportion of Annual Hours

17 , Proportion of Maximum Savings Savings Duration: Midwest Example MW Summer Winter Proportion of Annual Hours

18 , Proportion of Maximum Savings Savings Duration: Northeast Example MW Summer Winter Proportion of Annual Hours

19 , Proportion of Maximum Savings Proportion of AnnualHours MW Savings Duration: Southeast Example Summer Winter

20 Savings Proportions by End Use Cooking Water Heating HVAC Television Refrigeration Lighting Southwest Midwest Northeast Southeast

21 Results Summary Example Utility Peak-Hr MW % Savings Mean Savings Capacity Factor MWh % Savings Net change in Transactions MWh (%) Carbon Savings % Southwest Midwest Northeast Southeast

22 Relative Carbon Emissions Savings by Fuel Oil Coal Biomass Gas Southwest Midwest Northeast Southeast (CO lbs/mmbtu)

23 Conclusions and Discussion Large-scale ($- Billion) acquisition of efficiency provides baseload savings in during - percent of the year for utilities that might afford it Baseload savings amount to -% of maximum hourly savings (- MW in the example utilities) Modeling shows that utilities will sell excess generation saved by efficiency when possible reducing savings Starting point of load forecast matters results vary widely depending on forecast trends Barriers to large-scale acquisition are significant, but worth discussing and analyzing Next up: Commercial sector

24 Contact: Dave Anderson Feedback is requested and welcome